arXiv — NLP / Computation & Language · June 30, 2026 · 3 min read

A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignment

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Computer Science > Computation and Language

arXiv:2606.29273 (cs)

[Submitted on 28 Jun 2026]

Title:A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignment

Authors:Rashini Liyanarachchi, Frank Tran, Md Mahmudul Hasan, Aditya Joshi, Erik Meijering

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Abstract:Emotion recognition of song lyrics is a challenging task since lyrics may not necessarily align with the overall emotion of a song. As a result, lyrics annotation remains largely underexplored. Drawing inspiration from research in large language model (LLM) assisted annotation, we examine the alignment between humans and LLMs for annotation of lyrics by creating a new sentence-level dataset of lyrics. Our observations highlight the subjectivity of the task and the inherent challenges. Following this, we present a hybrid annotation framework that optimizes human and LLM annotation by predicting potential misalignment in annotation.

Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2606.29273 [cs.CL]
	(or arXiv:2606.29273v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2606.29273 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Rashini Liyanarachchi [view email]
[v1] Sun, 28 Jun 2026 08:41:19 UTC (792 KB)

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